pNeurFill: Enhanced Neural Network Model-Based Dummy Filling Synthesis With Perimeter Adjustment

Dummy filling is widely applied to significantly improve the planarity of topographic patterns for the chemical mechanical polishing (CMP) process in VLSI manufacturing. In the dummy filling flow, dummy synthesis works as the key step to adjust the post-CMP profile height. However, existing dummy sy...

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Vydáno v:IEEE transactions on computer-aided design of integrated circuits and systems Ročník 43; číslo 2; s. 1
Hlavní autoři: Chen, Zhaoting, Cai, Junzhe, Yan, Changhao, Bi, Zhaori, Ma, Yuzhe, Yu, Bei, Hu, Wenchuang, Zhou, Dian, Zeng, Xuan
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Chemical mechanical polishing
design for manufacturability
Integrated circuit modeling
Integrated circuits
Iterative methods
Layout
metal fill synthesis
Metals
Multiprocessing
neural network
Neural networks
Optimization
Quadratic programming
Semiconductor device modeling
sequential quadratic programming
Synthesis
Wires
ISSN:0278-0070, 1937-4151
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Shrnutí:Dummy filling is widely applied to significantly improve the planarity of topographic patterns for the chemical mechanical polishing (CMP) process in VLSI manufacturing. In the dummy filling flow, dummy synthesis works as the key step to adjust the post-CMP profile height. However, existing dummy synthesis optimization approaches usually fail to balance the filling quality and efficiency. This paper proposes a novel modelbased dummy filling synthesis framework NeurFill, integrated with multiple starting points-sequential quadratic programming (MSP-SQP) optimization solver. Inside this framework, a fullchip CMP simulator is first migrated to the neural network, achieving 8134× speedup on gradient calculation by backward propagation. Entrenched in the CMP neural network models, we further implement an improved version of NeurFill (pNeurFill) to alleviate the post-CMP height variation caused by dummy perimeter. After each iteration of dummy density optimization, an additional perimeter adjustment based on a given candidate dummy pattern set is applied to search for the optimal perimeter fill amount. The experimental results show that the proposed NeurFill outperforms existing rule-and model-based methods. The extra perimeter adjustment strategy in pNeurFill can achieve an average 66:97 Å decreasing in height variation and 8.92 quality improvement compared to NeurFill. This will provide guidance for DFM so as to increase IC chip yield.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0278-0070
1937-4151
DOI:10.1109/TCAD.2023.3320629